Mobile Video Streaming over Dynamic Single-Frequency Networks

The demand for multimedia streaming over mobile networks has been steadily increasing over the past several years. For instance, it has become common for mobile users to stream full TV episodes, sports events, and movies while on the go. Unfortunately, this growth in demand has strained the wireless networks despite the significant increase of their capacities with recent generations. Hence, efficient utilization of the expensive and limited wireless spectrum remains an important problem, especially in the context of multimedia streaming services that consume a large portion of the bandwidth capacity. In this article, we introduce the idea of dynamically configuring cells in wireless cellular networks to form single-frequency networks based on the multimedia traffic demands from users in each cell. We formulate the resource allocation problem in such complex networks with the goal of maximizing the number of served multimedia streams, and we prove that this problem is NP-Complete. Then we present an optimal solution to maximize the number of served multimedia streams within a cellular network. This optimal solution, however, may suffer from an exponential time complexity in the worst case, which is not practical for real-time streaming over large-scale networks. Therefore, we propose a heuristic algorithm with polynomial running time to provide faster and more practical solution for real-time deployments. Through detailed packet-level simulations, we assess the performance of the proposed algorithms with respect to the average service ratio, energy saving, video quality, frame loss rate, initial buffering time, rate of re-buffering events, and bandwidth overhead. We show that the proposed algorithms achieve substantial improvements in all of these performance metrics compared to the state-of-the-art approaches. For example, for the service ratio metric, our algorithms can serve up to 11 times more users compared to the unicast approach, and they achieve up to 54% improvement over the closest multicast approaches in the literature.